4.6 Article

Patent Analysis Using Bayesian Data Analysis and Network Modeling

期刊

APPLIED SCIENCES-BASEL
卷 12, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/app12031423

关键词

Bayesian additive regression trees; keyword visualization; management of technology; multiple linear regression; technology analysis

资金

  1. National Research Foundation of Korea (NRF) - Republic of Korea government (MSIT) [NRF-2020R1A2C1005918]

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This paper proposes a statistical method for quantitative patent analysis and applies it to analyze drone technology. By transforming patent documents into structured data using text mining techniques and applying Bayesian additive regression trees for analysis, technology scenarios for drones are constructed.
Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology.

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